Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

This work investigates the effect of children age on pragmatic skills, i.e. on the way children participate in conversations, in particular when it comes to turn-management (who talks when and how much) and use of silences and pauses. The proposed approach combines the extraction of “Steady Conversational Periods” time intervals during which the structure of a conversation is stable with Observed Influence Models, Generative Score Spaces and feature selection strategies. The experiments involve 76 children split into two age groups: “pre-School” (3-4 years) and “School” (6-8 years). The statistical approach proposed in this work predicts the group each child belongs to with precision up to 85%. Furthermore, it identifies the pragmatic skills that better account for the difference between the two groups.